Near-Lossless Compression for Large Traffic Networks

With advancements in sensor technologies, intelligent transportation systems can collect traffic data with high spatial and temporal resolution. However, the size of the networks combined with the huge volume of the data puts serious constraints on system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this paper, the authors analyze the reconstruction efficiency of several low-dimensional models for large and diverse networks. The compression performed by low-dimensional models is lossy in nature. To address this issue, the authors propose a near-lossless compression method for traffic data by applying the principle of lossy plus residual coding. To this end, they first develop a low-dimensional model of the network. They then apply Huffman coding (HC) in the residual layer. The resultant algorithm guarantees that the maximum reconstruction error will remain below a desired tolerance limit. For analysis, the authors consider a large and heterogeneous test network comprising of more than 18 000 road segments. The results show that the proposed method can efficiently compress data obtained from a large and diverse road network, while maintaining the upper bound on the reconstruction error.

• Record URL:
  https://doi.org/10.1109/TITS.2014.2374335
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Asif, Muhammad Tayyab
  • Srinivasan, Kannan
  • Mitrovic, Nikola
  • Dauwels, Justin
  • Jaillet, Patrick
• Publication Date: 2015-8

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1817-1826
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 16
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Computer algorithms; Data compression; Geometric segments; Networks; Traffic data; Traffic models
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I70: Traffic and Transport;

Filing Info

• Accession Number: 01586649
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Jan 7 2016 9:23AM